Fetching the paper…
Reading the bibliography…
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance.
Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
Earlier work this paper cites.
Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. 2020 · 2004
Earlier work this paper cites.
Named entity recognition without labelled data: A weak supervision approach
Pierre Lison, Aliaksandr Hubin, Jeremy Barnes, and Samia Touileb. 2020 · 2004
Earlier work this paper cites.
Generating counter narratives against online hate speech: Data and strategies
Serra Sinem Tekiroglu, Yi-Ling Chung, and Marco Guerini. 2020 · 2004
Earlier work this paper cites.
A framework for learning predictive structures from multiple tasks and unlabeled data
Rie Kubota Ando and Tong Zhang. 2005 · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Scalable training of L 1 L_{1} -regularized log-linear models
Galen Andrew and Jianfeng Gao. 2007 · 2007
Earlier work this paper cites.
Crowdsourcing user studies with mechanical turk
Aniket Kittur, Ed H Chi, and Bongwon Suh. 2008 · 2008
Earlier work this paper cites.
Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’connor, Dan Jurafsky, and Andrew Y Ng. 2008 · 2008
Earlier work this paper cites.
Generalized expectation criteria for semi-supervised learning with weakly labeled data
Gideon S Mann and Andrew McCallum. 2010 · 2010
Earlier work this paper cites.
Knowledge-based weak supervision for information extraction of overlapping relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011 · 2011
Earlier work this paper cites.
Distant supervision for relation extraction with an incomplete knowledge base
Bonan Min, Ralph Grishman, Li Wan, Chang Wang, and David Gondek. 2013 · 2013
Earlier work this paper cites.
Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements
Todd Lingren, Louise Deleger, Katalin Molnar, Haijun Zhai, Jareen Meinzen-Derr, Megan Kaiser, Laura Stoutenborough, Qi Li, and Imre Solti. 2014 · 2014
Earlier work this paper cites.
Can large language models transform computational social science?
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2023 · 2014
Earlier work this paper cites.
Yara parser: A fast and accurate dependency parser
Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Semeval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016a · 2016
Earlier work this paper cites.
SemEval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016b · 2016
Earlier work this paper cites.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
Earlier work this paper cites.
The price of debiasing automatic metrics in natural language evaluation
Arun Tejasvi Chaganty, Stephen Mussman, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Weakly-supervised neural text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han. 2018 · 2018
Cited alongside, same era.
Conversations gone awry: Detecting early signs of conversational failure
Justine Zhang, Jonathan P Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Nithum Thain, and Dario Taraborelli. 2018 · 2018
Cited alongside, same era.
Snorkel drybell: A case study in deploying weak supervision at industrial scale
Stephen H Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alex Ratner, Braden Hancock, Houman Alborzi, et al. 2019 · 2019
Cited alongside, same era.
Weakal: Combining active learning and weak supervision
Julius Gonsior, Maik Thiele, and Wolfgang Lehner. 2020 · 2020
A survey on programmatic weak supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, and Alexander Ratner. 2022 · 2022
Later among the works it cites.
Will affective computing emerge from foundation models and general ai? a first evaluation on chatgpt
Mostafa M Amin, Erik Cambria, and Björn W Schuller. 2023 · 2023
Closest in time.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023 · 2023
Closest in time.
Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Eras: Improving the quality control in the annotation process for natural language processing tasks
Jonatas S Grosman, Pedro HT Furtado, Ariane MB Rodrigues, Guilherme G Schardong, Simone DJ Barbosa, and Hélio CV Lopes. 2020 · 2020
Cited alongside, same era.
Pareto-optimal quantized resnet is mostly 4-bit
AmirAli Abdolrashidi, Lisa Wang, Shivani Agrawal, Jonathan Malmaud, Oleg Rybakov, Chas Leichner, and Lukasz Lew. 2021 · 2021
Cited alongside, same era.
Models in the loop: Aiding crowdworkers with generative annotation assistants
Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, and Douwe Kiela. 2021 · 2021
Cited alongside, same era.
Want to reduce labeling cost? gpt-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021
Cited alongside, same era.
Synthbio: A case study in human-ai collaborative curation of text datasets
Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, and Sebastian Gehrmann. 2021 · 2021
Cited alongside, same era.
Creating training sets via weak indirect supervision
Jieyu Zhang, Bohan Wang, Xiangchen Song, Yujing Wang, Yaming Yang, Jing Bai, and Alexander Ratner. 2021 · 2021
Cited alongside, same era.
Is gpt-3 a good data annotator?
Bosheng Ding, Chengwei Qin, Linlin Liu, Lidong Bing, Shafiq Joty, and Boyang Li. 2022 · 2022
Cited alongside, same era.
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
Closest in time.
Fan Huang, Haewoon Kwak, and Jisun An. 2023 · 2023
Closest in time.
Consistency analysis of chatgpt
Myeongjun Jang and Thomas Lukasiewicz. 2023 · 2023
Closest in time.
Distill or annotate? cost-efficient fine-tuning of compact models
Junmo Kang, Wei Xu, and Alan Ritter. 2023 · 2023
Closest in time.
Large language models are state-of-the-art evaluators of translation quality
Tom Kocmi and Christian Federmann. 2023 · 2023
Closest in time.
Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023 · 2023
Closest in time.
Chatgpt: Beginning of an end of manual annotation? use case of automatic genre identification
Taja Kuzman, Nikola Ljubešić, and Igor Mozetič. 2023 · 2023
Closest in time.
Chatgpt survey: Performance on nlp datasets
Matúš Pikuliak. 2023 · 2023
Closest in time.
Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
Closest in time.
Testing the reliability of chatgpt for text annotation and classification: A cautionary remark
Michael Reiss. 2023 · 2023
Closest in time.
Evaluation of chatgpt as a question answering system for answering complex questions
Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi. 2023 · 2023
Closest in time.
Petter Törnberg. 2023 · 2023
Closest in time.
Is chatgpt a good nlg evaluator? a preliminary study
Jiaan Wang, Yunlong Liang, Fandong Meng, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023 · 2023
Closest in time.
Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert
Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023 · 2023
Closest in time.